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Register a new userSegregation, integration and balance of large-scale resting brain networks configure different cognitive abilities
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Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains unclear how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here, we use an eigenmode-based approach to identify hierarchical modules in functional brain networks, and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n=991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations, and highly flexible switching between them. Furthermore, we employ structural equation modelling to estimate general and domain-specific cognitive phenotypes from nine tasks, and demonstrate that network segregation, integration and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and individuals tendency towards balance supports better memory. Our findings provide a comprehensive and deep understanding of the brains functioning principles in supporting diverse functional demands and cognitive abilities, and advance modern network neuroscience theories of human cognition.
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What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD). Accordingly, structural balance theory (SBT) affirms that in the real-world signed networks, a link is remarkably influenced by each of its two nodes interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matter in ASD, besides and beyond hypo (hyper) pair connectivity. To address this, we explore triadic interactions in the rs-fMRI network of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing first-time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distribution of triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks. Last but not least, we observe that energy of the salient and the default mode networks are lower in autism, which may be a reflection of the difficulty in flexible behaviors. Altogether, these results highlight the potential value of SBT as a new perspective in functional connectivity studies, especially in neurodevelopmental disorders.
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